The Emotional Experiences of Chinese High School Students Learning English as a Second Language
Bibliographic record
Abstract
This study investigated the achievement emotions of Chinese first-year high school students learning English as a foreign language (EFL). We adopted Pekrun et al.’s (2023) three-dimensional taxonomy of achievement emotions and applied it to the English learning context. A total of 178 students responded to a 6-point Likert scale measuring EFL achievement emotions. Data were analyzed using SPSS 27.0. Multiple regression analysis was conducted to identify emotional factors influencing students’ achievement scores. Canonical correlation analysis was employed to examine the relationships between the following sets of variables: a set of variables of foreign language outcome-retrospective emotions (FLORE); a set of variables of foreign language outcome-prospective emotions (FLOPE); and a set of variables of foreign language activity emotions (FLAE). The results showed that pride had a significant positive effect on learners’ English achievement scores, while anxiety had a significant negative effect. Canonical analysis revealed that the total redundancies among the three models were 22.8%, 26.2%, and 45.6%, respectively. This indicates that negative active emotions were positively associated with negative outcome-reflective and prospective negative emotions, whereas prospective positive emotions were linked to positive reflective emotions. The findings indicate that EFL learners who exhibited displeasure and faced challenges related to their competence in completing English tasks, both before and during English learning, tended to feel more anxiety and less pride. Conversely, students who anticipated academic success were more likely to feel pride in their achievement success. These findings suggest that task demands, cooperative English language learning activities, and effective communication regarding emotions promote positive emotions and reduce negative emotions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".